In recent years, on-demand content, including television programming and other episodic media content offered by over-the-top (OTT) and other media sources has become increasingly available to consumers. In the wake of this availability, consumers are increasingly watching multiple episodes of such episodic programming in rapid succession, a type of media interaction referred to as binge watching. When binge watching programming, consumers often desire to watch only the portion of the programming providing new content for the present episode (referred to herein as the “subject” portion or portions of the program), and may be frustrated by having to watch other portions of the programming, such as opening credits, ending credits, scenes from the last episode provided at the start of a present episode, scenes from the next episode provided at the end of a present episode, etc. (referred to herein as the “non-subject” portion or portions of the program).
Content playback interfaces (e.g., television receivers, and the like, providing OTT streaming episodic media) do not tend to provide an automated approach to finding transition points in programming between the subject and non-subject portions of a program. For example, content playback interfaces providing OTT media tend not to include technology to automatically identify the transition time at which end credits begin rolling. Identifying such a transition time can appreciably improve a consumer's binge watching experience, for example, by facilitating the consumer being able to jump to the next episode as soon as the current episode ends (i.e., without having to wait for the end credits to roll, or without requiring other navigation, such as fast-forwarding, or navigating out to a menu screen).
Some current approaches to identifying transition times, such as an end credit start time, use crowdsourcing. With such approaches, one or more human users can manually identify the transition time and can submit the identified time to a system. The system can then record the time as the transition time (or can process multiple received candidate times, and use a statistical or other technique to determine an appropriate transition time). Such approaches can be limited in a number of ways. For example, implementations relying on multiple sources of feedback may not obtain sufficient amounts of feedback quickly enough to be useful in many instances (e.g., for new or unpopular television programs). Further, the accuracy of such approaches can be subject to human error (e.g., in identifying timing, in data entry, etc.), to fuzziness and unpredictability in the turnout and/or accuracy of human users, to wait times for crowd responses, etc. Some such approaches can also depend on offering sufficient incentives to obtain accurate, timely results.